Project 4: Modeling neoplastic progression and analyzing genomic data to characterize the load of driver and passenger mutations in cancer. The development ofcancer can be considered as an evolutionary process within an organism. During neoplastic progression, cells acquire mutations, compete for resources, and are subject of selection for ability to grow fast in a complex and dynamic environment. These processes of Darwinian evolution have been systematically studied and modeled mathematically by methods similar to those used in Statistical Mechanics and related fields of Physics. As a result of somatic micro-evolution a population of cancer cells harbors multiple mutations: a few driver mutations essential for neoplastic progression, and many more passenger mutations. Classical works in population genetics have demonstrated that accumulation of mutations lead to increased genetic load, i.e. reduction in the mean fitness, and can lead to population extinction. Our hypothesis is that increasing the genetic load of mutations present in the population of cancer cells can make the population shrink to extinction, a process that we call clonal extinction. This idea can pave a way to a potentially novel approach to cancer treatment by therapeutics that unleash the deleterious effects of harbored mutations. To the best of our knowledge the idea of exploiting accumulated mutations to push a population of cancer cells into extinction has not been put forward. We propose to test this hypothesis by a unique approach that combines development of a theory of neoplastic evolution, analysis of emerging massive cancer genomics data, and experimental study of passenger mutation and their impact of cancer development. The experiment will reproduce seminal Luria-Delbruck experiment applied to cancer cell. Our data-driven theoretical approach is rooted in fifty years of evolutionary genetic thought about the genetic load and its consequences for population extinction.